Overview

Dataset statistics

Number of variables27
Number of observations34706
Missing cells63990
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory216.0 B

Variable types

Text1
DateTime1
Numeric19
Categorical6

Alerts

S2 has 4583 (13.2%) missing valuesMissing
S4 has 3532 (10.2%) missing valuesMissing
S5 has 18395 (53.0%) missing valuesMissing
S6 has 18395 (53.0%) missing valuesMissing
S7 has 18395 (53.0%) missing valuesMissing
S2 is highly skewed (γ1 = 51.10741875)Skewed
phq4_score has 11884 (34.2%) zerosZeros
phq2_score has 14164 (40.8%) zerosZeros
gad2_score has 18270 (52.6%) zerosZeros
P1 has 7479 (21.5%) zerosZeros
P2 has 18754 (54.0%) zerosZeros
P3 has 6787 (19.6%) zerosZeros
P4 has 25384 (73.1%) zerosZeros
S1 has 1811 (5.2%) zerosZeros
S3 has 18238 (52.5%) zerosZeros
S4 has 1479 (4.3%) zerosZeros
S5 has 700 (2.0%) zerosZeros
S6 has 700 (2.0%) zerosZeros

Reproduction

Analysis started2024-05-24 17:39:03.731278
Analysis finished2024-05-24 17:39:21.043009
Duration17.31 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

uid
Text

Distinct220
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:21.199125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1110592
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row3569e2f520db9014b4acc4227a6421c1
2nd row3569e2f520db9014b4acc4227a6421c1
3rd row3569e2f520db9014b4acc4227a6421c1
4th row3569e2f520db9014b4acc4227a6421c1
5th row3569e2f520db9014b4acc4227a6421c1
ValueCountFrequency (%)
aeeb186fafcc356f44cae870555f4a0d 441
 
1.3%
862f10a8c357e957a59d122077b3a5ad 436
 
1.3%
c37f9221f44e9ca35a49180dc05a7587 417
 
1.2%
73e13f8273906f7f43a077f95ec48e7d 388
 
1.1%
7d2c632a05bbb03ca97555d61be83c41 383
 
1.1%
1d2263527eed2a54e88d340fb8e55308 377
 
1.1%
35cf1abf179310dc33907d953f590366 372
 
1.1%
46b53cdf4d639d54e894d92b6dff817f 349
 
1.0%
bd6de07de02a8c2e98018a1c7daecc87 344
 
1.0%
ffc4b142e017c162ed4db7b05414fc4b 335
 
1.0%
Other values (210) 30864
88.9%
2024-05-24T10:39:21.442631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1110592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 79812
 
7.2%
3 73025
 
6.6%
5 71714
 
6.5%
1 71009
 
6.4%
d 70849
 
6.4%
7 70286
 
6.3%
c 69623
 
6.3%
a 69321
 
6.2%
e 68788
 
6.2%
4 68442
 
6.2%
Other values (6) 397723
35.8%
Distinct1749
Distinct (%)5.0%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
Minimum2017-09-08 00:00:00
Maximum2022-06-25 00:00:00
2024-05-24T10:39:21.522324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:21.582317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

pam
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.2572186
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:21.631325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile15
Maximum16
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3295091
Coefficient of variation (CV)0.59657967
Kurtosis-0.98789293
Mean7.2572186
Median Absolute Deviation (MAD)3
Skewness0.29796348
Sum251840
Variance18.744649
MonotonicityNot monotonic
2024-05-24T10:39:21.673002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 5381
15.5%
3 5167
14.9%
10 3681
10.6%
1 3297
9.5%
4 2713
7.8%
13 2704
7.8%
9 2191
 
6.3%
14 1335
 
3.8%
8 1258
 
3.6%
2 1235
 
3.6%
Other values (6) 5740
16.5%
ValueCountFrequency (%)
1 3297
9.5%
2 1235
 
3.6%
3 5167
14.9%
4 2713
7.8%
5 1064
 
3.1%
6 1125
 
3.2%
7 5381
15.5%
8 1258
 
3.6%
9 2191
6.3%
10 3681
10.6%
ValueCountFrequency (%)
16 1162
 
3.3%
15 864
 
2.5%
14 1335
 
3.8%
13 2704
7.8%
12 1221
 
3.5%
11 304
 
0.9%
10 3681
10.6%
9 2191
6.3%
8 1258
 
3.6%
7 5381
15.5%

phq4_score
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.3005879
Minimum0
Maximum12
Zeros11884
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:21.715649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6201541
Coefficient of variation (CV)1.1389063
Kurtosis2.2172468
Mean2.3005879
Median Absolute Deviation (MAD)2
Skewness1.4740684
Sum79835
Variance6.8652076
MonotonicityNot monotonic
2024-05-24T10:39:21.761500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11884
34.2%
1 5132
14.8%
4 4854
14.0%
2 4725
 
13.6%
3 3168
 
9.1%
5 1266
 
3.6%
6 1009
 
2.9%
8 822
 
2.4%
7 606
 
1.7%
12 484
 
1.4%
Other values (3) 752
 
2.2%
ValueCountFrequency (%)
0 11884
34.2%
1 5132
14.8%
2 4725
 
13.6%
3 3168
 
9.1%
4 4854
14.0%
5 1266
 
3.6%
6 1009
 
2.9%
7 606
 
1.7%
8 822
 
2.4%
9 327
 
0.9%
ValueCountFrequency (%)
12 484
 
1.4%
11 130
 
0.4%
10 295
 
0.8%
9 327
 
0.9%
8 822
 
2.4%
7 606
 
1.7%
6 1009
 
2.9%
5 1266
 
3.6%
4 4854
14.0%
3 3168
9.1%

phq2_score
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.2501009
Minimum0
Maximum6
Zeros14164
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:21.799886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4093797
Coefficient of variation (CV)1.1274128
Kurtosis1.835754
Mean1.2501009
Median Absolute Deviation (MAD)1
Skewness1.3342404
Sum43381
Variance1.9863512
MonotonicityNot monotonic
2024-05-24T10:39:21.841374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14164
40.8%
2 9389
27.1%
1 6857
19.8%
4 1605
 
4.6%
3 1515
 
4.4%
6 921
 
2.7%
5 251
 
0.7%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 14164
40.8%
1 6857
19.8%
2 9389
27.1%
3 1515
 
4.4%
4 1605
 
4.6%
5 251
 
0.7%
6 921
 
2.7%
ValueCountFrequency (%)
6 921
 
2.7%
5 251
 
0.7%
4 1605
 
4.6%
3 1515
 
4.4%
2 9389
27.1%
1 6857
19.8%
0 14164
40.8%

gad2_score
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.0496801
Minimum0
Maximum6
Zeros18270
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:21.879768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4363969
Coefficient of variation (CV)1.3684139
Kurtosis2.2450056
Mean1.0496801
Median Absolute Deviation (MAD)0
Skewness1.5696332
Sum36426
Variance2.063236
MonotonicityNot monotonic
2024-05-24T10:39:21.919505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 18270
52.6%
2 6907
 
19.9%
1 5365
 
15.5%
3 1520
 
4.4%
4 1401
 
4.0%
6 888
 
2.6%
5 351
 
1.0%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 18270
52.6%
1 5365
 
15.5%
2 6907
 
19.9%
3 1520
 
4.4%
4 1401
 
4.0%
5 351
 
1.0%
6 888
 
2.6%
ValueCountFrequency (%)
6 888
 
2.6%
5 351
 
1.0%
4 1401
 
4.0%
3 1520
 
4.4%
2 6907
 
19.9%
1 5365
 
15.5%
0 18270
52.6%

social_level
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
3.0
9621 
4.0
8021 
2.0
6916 
5.0
6019 
1.0
4125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters104106
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 9621
27.7%
4.0 8021
23.1%
2.0 6916
19.9%
5.0 6019
17.3%
1.0 4125
11.9%
(Missing) 4
 
< 0.1%

Length

2024-05-24T10:39:21.964124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.005627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 9621
27.7%
4.0 8021
23.1%
2.0 6916
19.9%
5.0 6019
17.3%
1.0 4125
11.9%

Most occurring characters

ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
3 9621
 
9.2%
4 8021
 
7.7%
2 6916
 
6.6%
5 6019
 
5.8%
1 4125
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
3 9621
 
9.2%
4 8021
 
7.7%
2 6916
 
6.6%
5 6019
 
5.8%
1 4125
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
3 9621
 
9.2%
4 8021
 
7.7%
2 6916
 
6.6%
5 6019
 
5.8%
1 4125
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
3 9621
 
9.2%
4 8021
 
7.7%
2 6916
 
6.6%
5 6019
 
5.8%
1 4125
 
4.0%

sse_score
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.047548
Minimum4
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:22.127276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q111
median13
Q315
95-th percentile18
Maximum20
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1918545
Coefficient of variation (CV)0.24463253
Kurtosis0.038608081
Mean13.047548
Median Absolute Deviation (MAD)2
Skewness-0.19702515
Sum452776
Variance10.187935
MonotonicityNot monotonic
2024-05-24T10:39:22.171167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12 4980
14.3%
13 4364
12.6%
14 3998
11.5%
16 3353
9.7%
11 3155
9.1%
15 3034
8.7%
10 2624
7.6%
17 2620
7.5%
9 1596
 
4.6%
8 1146
 
3.3%
Other values (7) 3832
11.0%
ValueCountFrequency (%)
4 352
 
1.0%
5 288
 
0.8%
6 341
 
1.0%
7 591
 
1.7%
8 1146
 
3.3%
9 1596
 
4.6%
10 2624
7.6%
11 3155
9.1%
12 4980
14.3%
13 4364
12.6%
ValueCountFrequency (%)
20 931
 
2.7%
19 564
 
1.6%
18 765
 
2.2%
17 2620
7.5%
16 3353
9.7%
15 3034
8.7%
14 3998
11.5%
13 4364
12.6%
12 4980
14.3%
11 3155
9.1%

stress
Categorical

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
2.0
11786 
3.0
9892 
1.0
6616 
4.0
4691 
5.0
1717 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters104106
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 11786
34.0%
3.0 9892
28.5%
1.0 6616
19.1%
4.0 4691
 
13.5%
5.0 1717
 
4.9%
(Missing) 4
 
< 0.1%

Length

2024-05-24T10:39:22.219410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.259798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 11786
34.0%
3.0 9892
28.5%
1.0 6616
19.1%
4.0 4691
 
13.5%
5.0 1717
 
4.9%

Most occurring characters

ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
2 11786
 
11.3%
3 9892
 
9.5%
1 6616
 
6.4%
4 4691
 
4.5%
5 1717
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
2 11786
 
11.3%
3 9892
 
9.5%
1 6616
 
6.4%
4 4691
 
4.5%
5 1717
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
2 11786
 
11.3%
3 9892
 
9.5%
1 6616
 
6.4%
4 4691
 
4.5%
5 1717
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 34702
33.3%
0 34702
33.3%
2 11786
 
11.3%
3 9892
 
9.5%
1 6616
 
6.4%
4 4691
 
4.5%
5 1717
 
1.6%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
F
23438 
M
10990 
both
 
274

Length

Max length4
Median length1
Mean length1.0236874
Min length1

Characters and Unicode

Total characters35524
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowboth
2nd rowboth
3rd rowboth
4th rowboth
5th rowboth

Common Values

ValueCountFrequency (%)
F 23438
67.5%
M 10990
31.7%
both 274
 
0.8%
(Missing) 4
 
< 0.1%

Length

2024-05-24T10:39:22.308835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.348980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
f 23438
67.5%
m 10990
31.7%
both 274
 
0.8%

Most occurring characters

ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 23438
66.0%
M 10990
30.9%
b 274
 
0.8%
o 274
 
0.8%
t 274
 
0.8%
h 274
 
0.8%

race
Categorical

Distinct8
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size271.3 KiB
white
20817 
asian
9002 
more than one
 
1475
other/hispanic
 
1364
black
 
1334
Other values (3)
 
710

Length

Max length29
Median length5
Mean length6.0893608
Min length5

Characters and Unicode

Total characters211313
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 20817
60.0%
asian 9002
25.9%
more than one 1475
 
4.2%
other/hispanic 1364
 
3.9%
black 1334
 
3.8%
american indian/alaska native 322
 
0.9%
alaskan native/white 209
 
0.6%
american indian/white 179
 
0.5%
(Missing) 4
 
< 0.1%

Length

2024-05-24T10:39:22.392201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.436419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
white 20817
53.8%
asian 9002
23.3%
more 1475
 
3.8%
than 1475
 
3.8%
one 1475
 
3.8%
other/hispanic 1364
 
3.5%
black 1334
 
3.4%
american 501
 
1.3%
indian/alaska 322
 
0.8%
native 322
 
0.8%
Other values (3) 597
 
1.5%

Most occurring characters

ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 211313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 34969
16.5%
e 26551
12.6%
a 25804
12.2%
h 25408
12.0%
t 24575
11.6%
w 21205
10.0%
n 15559
7.4%
s 10897
 
5.2%
o 4314
 
2.0%
3982
 
1.9%
Other values (10) 18049
8.5%

OS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size271.3 KiB
iOS
26711 
Android
7995 

Length

Max length7
Median length3
Mean length3.9214545
Min length3

Characters and Unicode

Total characters136098
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndroid
2nd rowAndroid
3rd rowAndroid
4th rowAndroid
5th rowAndroid

Common Values

ValueCountFrequency (%)
iOS 26711
77.0%
Android 7995
 
23.0%

Length

2024-05-24T10:39:22.491796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.531333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ios 26711
77.0%
android 7995
 
23.0%

Most occurring characters

ValueCountFrequency (%)
i 34706
25.5%
O 26711
19.6%
S 26711
19.6%
d 15990
11.7%
A 7995
 
5.9%
n 7995
 
5.9%
r 7995
 
5.9%
o 7995
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 34706
25.5%
O 26711
19.6%
S 26711
19.6%
d 15990
11.7%
A 7995
 
5.9%
n 7995
 
5.9%
r 7995
 
5.9%
o 7995
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 34706
25.5%
O 26711
19.6%
S 26711
19.6%
d 15990
11.7%
A 7995
 
5.9%
n 7995
 
5.9%
r 7995
 
5.9%
o 7995
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 34706
25.5%
O 26711
19.6%
S 26711
19.6%
d 15990
11.7%
A 7995
 
5.9%
n 7995
 
5.9%
r 7995
 
5.9%
o 7995
 
5.9%

cohort_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size271.3 KiB
2018.0
17598 
2017.0
17106 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters208224
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017.0
2nd row2017.0
3rd row2017.0
4th row2017.0
5th row2017.0

Common Values

ValueCountFrequency (%)
2018.0 17598
50.7%
2017.0 17106
49.3%
(Missing) 2
 
< 0.1%

Length

2024-05-24T10:39:22.571446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T10:39:22.607819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2018.0 17598
50.7%
2017.0 17106
49.3%

Most occurring characters

ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 69408
33.3%
2 34704
16.7%
1 34704
16.7%
. 34704
16.7%
8 17598
 
8.5%
7 17106
 
8.2%

P1
Real number (ℝ)

ZEROS 

Distinct25201
Distinct (%)72.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean8853.9435
Minimum0
Maximum43029.5
Zeros7479
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:22.652558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13983
median9494.1
Q313267.45
95-th percentile18520.345
Maximum43029.5
Range43029.5
Interquartile range (IQR)9284.45

Descriptive statistics

Standard deviation6157.9781
Coefficient of variation (CV)0.69550682
Kurtosis-0.7128497
Mean8853.9435
Median Absolute Deviation (MAD)4248.25
Skewness0.023173334
Sum3.0724955 × 108
Variance37920695
MonotonicityNot monotonic
2024-05-24T10:39:22.704041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7479
 
21.5%
11853.4 4
 
< 0.1%
13226.2 4
 
< 0.1%
11695.9 4
 
< 0.1%
10737.7 4
 
< 0.1%
11788.3 4
 
< 0.1%
13702 4
 
< 0.1%
12251.2 4
 
< 0.1%
14201 4
 
< 0.1%
5609.6 3
 
< 0.1%
Other values (25191) 27188
78.3%
(Missing) 4
 
< 0.1%
ValueCountFrequency (%)
0 7479
21.5%
0.5 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.9 1
 
< 0.1%
1.333333333 2
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 1
 
< 0.1%
3.833333333 1
 
< 0.1%
5.9 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
43029.5 1
< 0.1%
32758.1 1
< 0.1%
32734.5 1
< 0.1%
32095.9 1
< 0.1%
32066.1 1
< 0.1%
31683.75 1
< 0.1%
31503 1
< 0.1%
30850.7 1
< 0.1%
30841 1
< 0.1%
30607.9 1
< 0.1%

P2
Real number (ℝ)

ZEROS 

Distinct14840
Distinct (%)43.0%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.6377419
Minimum0
Maximum23.002912
Zeros18754
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:22.753968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.6044722
95-th percentile6.5410454
Maximum23.002912
Range23.002912
Interquartile range (IQR)2.6044722

Descriptive statistics

Standard deviation2.8707786
Coefficient of variation (CV)1.7528883
Kurtosis11.858651
Mean1.6377419
Median Absolute Deviation (MAD)0
Skewness2.9667358
Sum56582.345
Variance8.24137
MonotonicityNot monotonic
2024-05-24T10:39:22.805378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18754
54.0%
0.05 33
 
0.1%
0.1666944444 24
 
0.1%
0.1166666667 18
 
0.1%
0.08336111111 16
 
< 0.1%
0.2000277778 15
 
< 0.1%
0.1166944444 14
 
< 0.1%
0.1000277778 12
 
< 0.1%
0.06669444444 12
 
< 0.1%
0.06666666667 12
 
< 0.1%
Other values (14830) 15639
45.1%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 18754
54.0%
0.01666666667 1
 
< 0.1%
0.03333333333 2
 
< 0.1%
0.03336111111 1
 
< 0.1%
0.03361111111 2
 
< 0.1%
0.03427777778 2
 
< 0.1%
0.03441666667 1
 
< 0.1%
0.03461111111 1
 
< 0.1%
0.03477777778 2
 
< 0.1%
0.03513888889 1
 
< 0.1%
ValueCountFrequency (%)
23.00291222 1
< 0.1%
22.10058333 1
< 0.1%
21.96547222 1
< 0.1%
21.73336111 1
< 0.1%
21.68663889 1
< 0.1%
21.63622222 1
< 0.1%
21.53913889 1
< 0.1%
21.25377778 1
< 0.1%
21.23377683 1
< 0.1%
21.14190631 1
< 0.1%

P3
Real number (ℝ)

ZEROS 

Distinct27578
Distinct (%)79.8%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean9.751511
Minimum0
Maximum23.981444
Zeros6787
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:22.858144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.7496944
median10.752083
Q314.643222
95-th percentile20.089128
Maximum23.981444
Range23.981444
Interquartile range (IQR)10.893528

Descriptive statistics

Standard deviation6.6300403
Coefficient of variation (CV)0.67989876
Kurtosis-1.0317415
Mean9.751511
Median Absolute Deviation (MAD)4.78625
Skewness-0.12397822
Sum336904.95
Variance43.957434
MonotonicityNot monotonic
2024-05-24T10:39:22.913066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6787
 
19.6%
0.05 4
 
< 0.1%
3.020305556 4
 
< 0.1%
0.1704258611 4
 
< 0.1%
0.1705490556 4
 
< 0.1%
1.673934389 4
 
< 0.1%
1.2495 3
 
< 0.1%
9.223555556 3
 
< 0.1%
2.249889 3
 
< 0.1%
3.044055556 3
 
< 0.1%
Other values (27568) 27730
79.9%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 6787
19.6%
0.01666666667 1
 
< 0.1%
0.03327777778 1
 
< 0.1%
0.03330555556 1
 
< 0.1%
0.03375155556 1
 
< 0.1%
0.03380461111 1
 
< 0.1%
0.03447222222 2
 
< 0.1%
0.03658333333 1
 
< 0.1%
0.03944444444 1
 
< 0.1%
0.03961111111 1
 
< 0.1%
ValueCountFrequency (%)
23.98144444 1
< 0.1%
23.97108333 1
< 0.1%
23.97080556 1
< 0.1%
23.9443585 1
< 0.1%
23.93147222 1
< 0.1%
23.92102778 1
< 0.1%
23.90494444 1
< 0.1%
23.90466667 1
< 0.1%
23.90455556 1
< 0.1%
23.90455556 1
< 0.1%

P4
Real number (ℝ)

ZEROS 

Distinct6538
Distinct (%)18.9%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.15353227
Minimum0
Maximum14.685222
Zeros25384
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:22.965760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.066666667
95-th percentile0.87479444
Maximum14.685222
Range14.685222
Interquartile range (IQR)0.066666667

Descriptive statistics

Standard deviation0.46047347
Coefficient of variation (CV)2.9991966
Kurtosis104.85233
Mean0.15353227
Median Absolute Deviation (MAD)0
Skewness7.1917398
Sum5304.3864
Variance0.21203582
MonotonicityNot monotonic
2024-05-24T10:39:23.021251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25384
73.1%
0.06666666667 70
 
0.2%
0.05 67
 
0.2%
0.1166944444 60
 
0.2%
0.08333333333 53
 
0.2%
0.06669444444 51
 
0.1%
0.1 41
 
0.1%
0.15 38
 
0.1%
0.08336111111 38
 
0.1%
0.06663888889 37
 
0.1%
Other values (6528) 8710
 
25.1%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 25384
73.1%
0.01666716667 1
 
< 0.1%
0.03319444444 1
 
< 0.1%
0.03325 1
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03336111111 1
 
< 0.1%
0.03391666667 2
 
< 0.1%
0.03444444444 1
 
< 0.1%
0.03452777778 2
 
< 0.1%
0.03469444444 2
 
< 0.1%
ValueCountFrequency (%)
14.68522222 1
< 0.1%
12.69905556 1
< 0.1%
12.44058333 1
< 0.1%
10.28533333 1
< 0.1%
10.06680556 1
< 0.1%
8.640944444 1
< 0.1%
6.978666667 1
< 0.1%
6.862111111 1
< 0.1%
6.428333333 1
< 0.1%
6.181055556 1
< 0.1%

S1
Real number (ℝ)

ZEROS 

Distinct23471
Distinct (%)67.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2172.9635
Minimum0
Maximum25374.3
Zeros1811
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.076677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1288
median1403.3
Q33344.65
95-th percentile6762.61
Maximum25374.3
Range25374.3
Interquartile range (IQR)3056.65

Descriptive statistics

Standard deviation2387.247
Coefficient of variation (CV)1.0986135
Kurtosis5.4819674
Mean2172.9635
Median Absolute Deviation (MAD)1259.25
Skewness1.8113631
Sum75406178
Variance5698948.3
MonotonicityNot monotonic
2024-05-24T10:39:23.130647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1811
 
5.2%
19.5 19
 
0.1%
1.5 17
 
< 0.1%
5 15
 
< 0.1%
32.9 14
 
< 0.1%
3 12
 
< 0.1%
8.5 12
 
< 0.1%
1.6 12
 
< 0.1%
7.8 11
 
< 0.1%
18 11
 
< 0.1%
Other values (23461) 32768
94.4%
ValueCountFrequency (%)
0 1811
5.2%
0.3 3
 
< 0.1%
0.4 3
 
< 0.1%
0.5 2
 
< 0.1%
0.6 2
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 4
 
< 0.1%
0.9 4
 
< 0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
25374.3 1
< 0.1%
24673.4 1
< 0.1%
24092.2 1
< 0.1%
23886.5 1
< 0.1%
23354.2 1
< 0.1%
22334.4 1
< 0.1%
22123.7 1
< 0.1%
21248 1
< 0.1%
21071.5 1
< 0.1%
20450.9 1
< 0.1%

S2
Real number (ℝ)

MISSING  SKEWED 

Distinct29628
Distinct (%)98.4%
Missing4583
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean164481.14
Minimum0
Maximum1.4242156 × 108
Zeros267
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.184064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1159.08
Q15482.391
median22575.538
Q375181.498
95-th percentile463231.3
Maximum1.4242156 × 108
Range1.4242156 × 108
Interquartile range (IQR)69699.107

Descriptive statistics

Standard deviation1867988.4
Coefficient of variation (CV)11.356855
Kurtosis3493.9396
Mean164481.14
Median Absolute Deviation (MAD)19557.114
Skewness51.107419
Sum4.9546653 × 109
Variance3.4893807 × 1012
MonotonicityNot monotonic
2024-05-24T10:39:23.239734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 267
 
0.8%
417.376509 6
 
< 0.1%
73.81279146 4
 
< 0.1%
0.8144253537 4
 
< 0.1%
9.796842445 3
 
< 0.1%
9.321555283 3
 
< 0.1%
11.48979321 3
 
< 0.1%
137.1731203 3
 
< 0.1%
2154.098184 3
 
< 0.1%
4773.407923 3
 
< 0.1%
Other values (29618) 29824
85.9%
(Missing) 4583
 
13.2%
ValueCountFrequency (%)
0 267
0.8%
0.3152189938 1
 
< 0.1%
0.3699870183 2
 
< 0.1%
0.3751709523 2
 
< 0.1%
0.8136327639 1
 
< 0.1%
0.8144253537 4
 
< 0.1%
1.032295243 2
 
< 0.1%
1.124663488 1
 
< 0.1%
1.371707153 1
 
< 0.1%
1.413705336 1
 
< 0.1%
ValueCountFrequency (%)
142421562.1 1
< 0.1%
142391822.1 1
< 0.1%
142380475.7 1
< 0.1%
55951647.87 1
< 0.1%
55914254.36 1
< 0.1%
42122126.02 1
< 0.1%
42121761.16 1
< 0.1%
42120759.64 1
< 0.1%
41827623.8 1
< 0.1%
41827212.35 1
< 0.1%

S3
Real number (ℝ)

ZEROS 

Distinct15222
Distinct (%)44.1%
Missing157
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.79524211
Minimum0
Maximum18.871861
Zeros18238
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.294894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.1855833
95-th percentile3.269146
Maximum18.871861
Range18.871861
Interquartile range (IQR)1.1855833

Descriptive statistics

Standard deviation1.4832008
Coefficient of variation (CV)1.8650933
Kurtosis26.750526
Mean0.79524211
Median Absolute Deviation (MAD)0
Skewness4.1012815
Sum27474.82
Variance2.1998845
MonotonicityNot monotonic
2024-05-24T10:39:23.350390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18238
52.5%
0.05 38
 
0.1%
0.1 21
 
0.1%
0.06666666667 19
 
0.1%
0.1666944444 18
 
0.1%
0.08333333333 18
 
0.1%
0.06669444444 17
 
< 0.1%
0.1166666667 15
 
< 0.1%
0.08336111111 14
 
< 0.1%
0.1166944444 14
 
< 0.1%
Other values (15212) 16137
46.5%
(Missing) 157
 
0.5%
ValueCountFrequency (%)
0 18238
52.5%
0.01687447222 1
 
< 0.1%
0.03288888889 2
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03438888889 1
 
< 0.1%
0.03505555556 2
 
< 0.1%
0.03616666667 2
 
< 0.1%
0.03691358025 1
 
< 0.1%
0.037 1
 
< 0.1%
0.03763888889 2
 
< 0.1%
ValueCountFrequency (%)
18.87186111 1
< 0.1%
17.45444525 1
< 0.1%
17.37983333 1
< 0.1%
17.30280556 1
< 0.1%
17.23944444 1
< 0.1%
17.19877292 1
< 0.1%
16.61297222 1
< 0.1%
16.59935728 1
< 0.1%
16.39552106 1
< 0.1%
16.35594444 1
< 0.1%

S4
Real number (ℝ)

MISSING  ZEROS 

Distinct275
Distinct (%)0.9%
Missing3532
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean3.4101619
Minimum0
Maximum11.3
Zeros1479
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.410067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33333333
Q11.8
median3.3
Q34.9
95-th percentile6.6
Maximum11.3
Range11.3
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9159857
Coefficient of variation (CV)0.56184596
Kurtosis-0.67241027
Mean3.4101619
Median Absolute Deviation (MAD)1.5
Skewness0.21288477
Sum106308.39
Variance3.6710011
MonotonicityNot monotonic
2024-05-24T10:39:23.464329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1505
 
4.3%
0 1479
 
4.3%
1.1 700
 
2.0%
1.3 619
 
1.8%
4 617
 
1.8%
1.2 578
 
1.7%
2 572
 
1.6%
3 547
 
1.6%
3.5 540
 
1.6%
3.1 532
 
1.5%
Other values (265) 23485
67.7%
(Missing) 3532
 
10.2%
ValueCountFrequency (%)
0 1479
4.3%
0.1 15
 
< 0.1%
0.1111111111 4
 
< 0.1%
0.125 3
 
< 0.1%
0.1428571429 2
 
< 0.1%
0.1666666667 6
 
< 0.1%
0.2 14
 
< 0.1%
0.2222222222 3
 
< 0.1%
0.25 20
 
0.1%
0.2857142857 3
 
< 0.1%
ValueCountFrequency (%)
11.3 2
< 0.1%
11.2 2
< 0.1%
11 2
< 0.1%
10.9 2
< 0.1%
10.8 1
 
< 0.1%
10.5 2
< 0.1%
10.3 2
< 0.1%
10.2 3
< 0.1%
10.1 2
< 0.1%
10 1
 
< 0.1%

S5
Real number (ℝ)

MISSING  ZEROS 

Distinct14985
Distinct (%)91.9%
Missing18395
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean12.50653
Minimum0
Maximum106.25492
Zeros700
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.524628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.19408376
Q15.8402466
median10.337346
Q316.551765
95-th percentile30.719688
Maximum106.25492
Range106.25492
Interquartile range (IQR)10.711518

Descriptive statistics

Standard deviation10.10011
Coefficient of variation (CV)0.80758694
Kurtosis7.6799989
Mean12.50653
Median Absolute Deviation (MAD)5.0941038
Skewness2.0471799
Sum203994.01
Variance102.01222
MonotonicityNot monotonic
2024-05-24T10:39:23.668044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 700
 
2.0%
5.650047975 4
 
< 0.1%
13.73271486 4
 
< 0.1%
67.58068606 4
 
< 0.1%
0.001666666667 4
 
< 0.1%
4.500825846 4
 
< 0.1%
52.97686122 4
 
< 0.1%
2.876171431 4
 
< 0.1%
18.69403 4
 
< 0.1%
2.210433882 4
 
< 0.1%
Other values (14975) 15575
44.9%
(Missing) 18395
53.0%
ValueCountFrequency (%)
0 700
2.0%
0.0002380385622 1
 
< 0.1%
0.0002542803865 1
 
< 0.1%
0.000322962644 1
 
< 0.1%
0.0003571853792 1
 
< 0.1%
0.0004168982768 2
 
< 0.1%
0.0004991680532 1
 
< 0.1%
0.0005 1
 
< 0.1%
0.0005036937542 1
 
< 0.1%
0.000508560773 1
 
< 0.1%
ValueCountFrequency (%)
106.2549212 1
 
< 0.1%
99.94049944 1
 
< 0.1%
98.42052744 1
 
< 0.1%
94.10399983 3
< 0.1%
93.4679085 2
< 0.1%
90 1
 
< 0.1%
89.50591965 1
 
< 0.1%
87.95779987 1
 
< 0.1%
84.79136164 1
 
< 0.1%
81.01231812 1
 
< 0.1%

S6
Real number (ℝ)

MISSING  ZEROS 

Distinct14848
Distinct (%)91.0%
Missing18395
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean10.398949
Minimum0
Maximum84.765226
Zeros700
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.719906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0807959
Q15.5596611
median8.9568025
Q313.589376
95-th percentile23.861803
Maximum84.765226
Range84.765226
Interquartile range (IQR)8.0297145

Descriptive statistics

Standard deviation7.3564186
Coefficient of variation (CV)0.70741942
Kurtosis5.526554
Mean10.398949
Median Absolute Deviation (MAD)3.8377391
Skewness1.6944187
Sum169617.26
Variance54.116895
MonotonicityNot monotonic
2024-05-24T10:39:23.771647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 700
 
2.0%
4 10
 
< 0.1%
2 7
 
< 0.1%
12 7
 
< 0.1%
6 7
 
< 0.1%
10.8 6
 
< 0.1%
8 6
 
< 0.1%
16 6
 
< 0.1%
14 5
 
< 0.1%
1.5 5
 
< 0.1%
Other values (14838) 15552
44.8%
(Missing) 18395
53.0%
ValueCountFrequency (%)
0 700
2.0%
0.08823096907 2
 
< 0.1%
0.1999666722 1
 
< 0.1%
0.2856462747 1
 
< 0.1%
0.2969337941 1
 
< 0.1%
0.3003003003 1
 
< 0.1%
0.3051364638 1
 
< 0.1%
0.3157833242 2
 
< 0.1%
0.3608205703 1
 
< 0.1%
0.3875551728 1
 
< 0.1%
ValueCountFrequency (%)
84.76522568 1
< 0.1%
80.9826534 1
< 0.1%
65.42315819 1
< 0.1%
65.18370983 1
< 0.1%
63.64779212 1
< 0.1%
60.33891108 1
< 0.1%
60.14958852 1
< 0.1%
58.50771186 1
< 0.1%
56.75107691 1
< 0.1%
56.73246604 1
< 0.1%

S7
Real number (ℝ)

MISSING 

Distinct15636
Distinct (%)95.9%
Missing18395
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean60.427442
Minimum3.5081967
Maximum119.98947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.826118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.5081967
5-th percentile34.681042
Q149.061972
median58.986266
Q371.35485
95-th percentile89.966285
Maximum119.98947
Range116.48128
Interquartile range (IQR)22.292878

Descriptive statistics

Standard deviation17.032139
Coefficient of variation (CV)0.28186099
Kurtosis0.27783495
Mean60.427442
Median Absolute Deviation (MAD)10.936662
Skewness0.30936948
Sum985632.01
Variance290.09375
MonotonicityNot monotonic
2024-05-24T10:39:23.886917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.97501041 5
 
< 0.1%
44.74972191 4
 
< 0.1%
26.29508197 4
 
< 0.1%
49.51323097 4
 
< 0.1%
39.04007395 4
 
< 0.1%
55.87110868 4
 
< 0.1%
59.97496871 4
 
< 0.1%
37.09803922 4
 
< 0.1%
59.98333333 4
 
< 0.1%
51.23845195 4
 
< 0.1%
Other values (15626) 16270
46.9%
(Missing) 18395
53.0%
ValueCountFrequency (%)
3.508196721 1
< 0.1%
3.66 1
< 0.1%
4.264297612 1
< 0.1%
4.380366915 1
< 0.1%
5.036319613 1
< 0.1%
6.866666667 1
< 0.1%
6.891891892 2
< 0.1%
7.206703911 1
< 0.1%
7.960032781 1
< 0.1%
7.977237455 1
< 0.1%
ValueCountFrequency (%)
119.9894746 2
< 0.1%
119.9886909 2
< 0.1%
119.9815993 1
< 0.1%
119.9698795 2
< 0.1%
119.968116 1
< 0.1%
119.9666277 1
< 0.1%
119.9652979 1
< 0.1%
119.9550067 1
< 0.1%
119.953411 1
< 0.1%
119.9500156 1
< 0.1%

Z1
Real number (ℝ)

Distinct1338
Distinct (%)3.9%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.46623
Minimum2.375
Maximum23.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:23.970116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2.375
5-th percentile4.9
Q16.375
median7.325
Q38.2
95-th percentile10.074821
Maximum23.75
Range21.375
Interquartile range (IQR)1.825

Descriptive statistics

Standard deviation2.049388
Coefficient of variation (CV)0.27448766
Kurtosis16.428298
Mean7.46623
Median Absolute Deviation (MAD)0.9
Skewness2.8655744
Sum259093.11
Variance4.1999912
MonotonicityNot monotonic
2024-05-24T10:39:24.024679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.45 302
 
0.9%
7.5 286
 
0.8%
7.125 280
 
0.8%
7.75 271
 
0.8%
7.175 270
 
0.8%
7 268
 
0.8%
7.475 262
 
0.8%
7.625 262
 
0.8%
7.775 261
 
0.8%
7.4 259
 
0.7%
Other values (1328) 31981
92.1%
ValueCountFrequency (%)
2.375 1
 
< 0.1%
2.45 1
 
< 0.1%
2.475 1
 
< 0.1%
2.5 1
 
< 0.1%
2.583333333 1
 
< 0.1%
2.6125 1
 
< 0.1%
2.625 3
< 0.1%
2.65 1
 
< 0.1%
2.666666667 1
 
< 0.1%
2.675 2
< 0.1%
ValueCountFrequency (%)
23.75 34
0.1%
23.71428571 1
 
< 0.1%
23.625 1
 
< 0.1%
23.5 5
 
< 0.1%
23.375 1
 
< 0.1%
23.35 1
 
< 0.1%
23.28571429 1
 
< 0.1%
23.25 4
 
< 0.1%
23.21428571 1
 
< 0.1%
23.11111111 1
 
< 0.1%

Z2
Real number (ℝ)

Distinct1104
Distinct (%)3.2%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.166525
Minimum0
Maximum130.8
Zeros265
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:24.086778image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.4
Q131.6
median39.6
Q348.2
95-th percentile62.2
Maximum130.8
Range130.8
Interquartile range (IQR)16.6

Descriptive statistics

Standard deviation13.449629
Coefficient of variation (CV)0.33484673
Kurtosis1.2190007
Mean40.166525
Median Absolute Deviation (MAD)8.2
Skewness0.26975867
Sum1393858.7
Variance180.89253
MonotonicityNot monotonic
2024-05-24T10:39:24.154820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265
 
0.8%
39.6 254
 
0.7%
38.4 248
 
0.7%
37.8 242
 
0.7%
36 239
 
0.7%
38.8 238
 
0.7%
39.2 238
 
0.7%
40.4 234
 
0.7%
36.6 233
 
0.7%
37.6 231
 
0.7%
Other values (1094) 32280
93.0%
ValueCountFrequency (%)
0 265
0.8%
0.4 3
 
< 0.1%
0.4444444444 1
 
< 0.1%
0.6 3
 
< 0.1%
0.6666666667 1
 
< 0.1%
1 4
 
< 0.1%
1.111111111 1
 
< 0.1%
1.2 1
 
< 0.1%
1.4 1
 
< 0.1%
1.428571429 1
 
< 0.1%
ValueCountFrequency (%)
130.8 1
< 0.1%
129.2 1
< 0.1%
122.6 1
< 0.1%
113.6 2
< 0.1%
112 1
< 0.1%
110.8 1
< 0.1%
107.4 1
< 0.1%
106.8 1
< 0.1%
106 1
< 0.1%
103.8 1
< 0.1%

Z3
Real number (ℝ)

Distinct1148
Distinct (%)3.3%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean99.896365
Minimum46
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size271.3 KiB
2024-05-24T10:39:24.222419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile83.2
Q192.2
median98.6
Q3105.4
95-th percentile119.8
Maximum190
Range144
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation12.974069
Coefficient of variation (CV)0.12987529
Kurtosis9.1705848
Mean99.896365
Median Absolute Deviation (MAD)6.6
Skewness1.8984033
Sum3466603.7
Variance168.32647
MonotonicityNot monotonic
2024-05-24T10:39:24.301107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.8 315
 
0.9%
99.4 301
 
0.9%
96.8 300
 
0.9%
100 297
 
0.9%
97.4 297
 
0.9%
99 290
 
0.8%
98.2 290
 
0.8%
99.2 290
 
0.8%
96 287
 
0.8%
93.4 286
 
0.8%
Other values (1138) 31749
91.5%
ValueCountFrequency (%)
46 1
< 0.1%
48.4 2
< 0.1%
48.66666667 1
< 0.1%
49.2 1
< 0.1%
54.6 2
< 0.1%
56 1
< 0.1%
56.2 1
< 0.1%
57.2 1
< 0.1%
58 2
< 0.1%
59 1
< 0.1%
ValueCountFrequency (%)
190 48
0.1%
189.7142857 2
 
< 0.1%
189 1
 
< 0.1%
188.2 1
 
< 0.1%
188 4
 
< 0.1%
187.1428571 2
 
< 0.1%
187.1111111 1
 
< 0.1%
187 2
 
< 0.1%
186.8 2
 
< 0.1%
186 5
 
< 0.1%

Interactions

2024-05-24T10:39:19.553138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.156858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.043664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.110401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.963987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.713759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.567673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.427224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.177437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.948186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.844935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.675170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.538268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.516209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.351546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.347346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.192218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.050967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.788385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.598021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.247051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.085019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.151708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.005721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.756858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.611739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.466494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.221515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.989863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.887094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-05-24T10:39:06.699042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.438856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.219262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.150410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.901855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.675574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.453076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.358061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.215933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.111716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.057660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.045558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.953053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.797877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.534029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.272572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.225952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.807033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.556320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.736288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.475685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.277919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.187846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.942544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.714902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.493774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.400502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.257163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.153085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.102075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.093866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.987190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.833512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.572660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.307682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.266410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.845654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.594295image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.772924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.514002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.337574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.229772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.980803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.752078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.534705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.449610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.297718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.192067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.147300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.138393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.022199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.870003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.613335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.343921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.304765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.881594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.630098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.809394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.548948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.382887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.267441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.018560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.788775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.575672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.495300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.335381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.232741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.190199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.180310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.054369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.904009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.646714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.378558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.345320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.922094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:05.925788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.848697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.589231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.433436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.313125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.058736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.827585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.718836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.544538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.408941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.375698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.233328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.226225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.090247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.943052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.682629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.420509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.381506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.957971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.024971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.883544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.630325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.477347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.348450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.095333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.865585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.758617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.585036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.448501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.421162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.268815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.263441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.121739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.976545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.715045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.457742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:20.421271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:04.998202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.066152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:06.923324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:07.673572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:08.521624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:09.388454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.136249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:10.905516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:11.801248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:12.629830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:13.489550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:14.466746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:15.308932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:16.306059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:17.155728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.012240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:18.750557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-24T10:39:19.507346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-05-24T10:39:20.492603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-24T10:39:20.655749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-24T10:39:20.910574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

uidday_surveypamphq4_scorephq2_scoregad2_scoresocial_levelsse_scorestressgenderraceOScohort_yearP1P2P3P4S1S2S3S4S5S6S7Z1Z2Z3
03569e2f520db9014b4acc4227a6421c12017-09-093.01.00.01.02.016.02.0bothwhiteAndroid2017.00.0NaNNaNNaN561.0000002767.287472NaN6.0NaNNaNNaN14.5000000.000000116.0
13569e2f520db9014b4acc4227a6421c12017-09-103.02.01.01.03.013.02.0bothwhiteAndroid2017.00.0NaNNaNNaN694.5000002737.717474NaN4.5NaNNaNNaN8.87500023.00000094.0
23569e2f520db9014b4acc4227a6421c12017-09-1412.01.01.00.02.013.02.0bothwhiteAndroid2017.00.05.67147314.1607180.710007483.1666675548.6118301.8967595.545.83006610.69013367.6459096.41666731.66666783.0
33569e2f520db9014b4acc4227a6421c12017-09-184.01.01.00.03.011.03.0bothwhiteAndroid2017.00.04.39533014.9691091.645656357.5000007865.0605481.3235025.534.39452310.77704661.9828945.60000039.00000083.8
43569e2f520db9014b4acc4227a6421c12017-09-229.01.01.00.04.014.02.0bothwhiteAndroid2017.00.04.22863515.2822541.636484308.0000008556.0293421.9751436.034.44704110.52175065.9766305.05000045.20000085.6
53569e2f520db9014b4acc4227a6421c12017-09-2411.01.01.00.03.014.03.0bothwhiteAndroid2017.00.03.68787113.4165021.811246713.30000014766.2314521.8057976.424.0230299.05891759.8074834.90000047.20000086.4
63569e2f520db9014b4acc4227a6421c12017-09-2710.00.00.00.04.015.02.0bothwhiteAndroid2017.00.03.13373412.4574811.155785862.50000019906.3980512.8712295.925.00092510.58214862.7424195.87500041.00000088.0
73569e2f520db9014b4acc4227a6421c12017-09-309.00.00.00.02.014.01.0bothwhiteAndroid2017.00.03.01712210.7916180.855095849.90000018808.5369902.3379486.627.6304759.88159157.9368395.80000039.80000086.2
83569e2f520db9014b4acc4227a6421c12017-10-0112.00.00.00.02.013.02.0bothwhiteAndroid2017.00.03.05168210.6880680.687200896.40000018723.6767772.3725236.429.0002429.65890957.7466435.57500043.60000088.2
93569e2f520db9014b4acc4227a6421c12017-10-069.00.00.00.03.013.02.0bothwhiteAndroid2017.00.03.63788312.7745710.187365384.1000006135.3131192.2000796.028.1186056.68311651.6789494.42500058.40000093.8
uidday_surveypamphq4_scorephq2_scoregad2_scoresocial_levelsse_scorestressgenderraceOScohort_yearP1P2P3P4S1S2S3S4S5S6S7Z1Z2Z3
346962c4f43b2212eee5ba69563f1399111382020-09-167.00.00.00.01.013.02.0MwhiteiOS2018.011695.40.02.5193060.07504.8211140.9248180.0000000.5NaNNaNNaN7.87539.2102.2
346972c4f43b2212eee5ba69563f1399111382020-09-243.00.00.00.01.013.02.0MwhiteiOS2018.011295.80.00.0000000.061.14131.1991010.0000000.9NaNNaNNaN7.02536.492.6
346982c4f43b2212eee5ba69563f1399111382020-09-267.00.00.00.05.012.02.0MwhiteiOS2018.010816.20.00.0000000.0324.17439.4678050.0000001.3NaNNaNNaN7.12537.694.6
346992c4f43b2212eee5ba69563f1399111382020-10-105.00.00.00.03.013.02.0MwhiteiOS2018.012388.60.00.0000000.0817.214194.9486870.1834442.75.5300557.16107126.441067.65038.699.8
347002c4f43b2212eee5ba69563f1399111382020-10-145.00.00.00.05.015.02.0MwhiteiOS2018.011105.40.01.0161670.01918.333832.9111970.1834442.85.5300557.16107126.441067.85041.2104.0
347012c4f43b2212eee5ba69563f1399111382020-12-243.00.00.00.04.015.01.0MwhiteiOS2018.022771.30.08.4938890.03879.767682.6349930.0000002.5NaNNaNNaN5.95033.681.2
347028617ddac1f48b148e3683738519b2e7aNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNiOS2018.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34703ea716dd032aaa0dcf8bfa36b1811917fNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNiOS2017.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34704df5e798581def8d477316520953b9171NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAndroidNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
34705e6d71fe4a3c10b075ae1cf51a2fe6cfdNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNiOSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN